 So, right now we can, for about a thousand euros, get sequence of human genome, and we can sequence, as I've mentioned, bacterial genomes for really just dollars. So what do we do with that information? Well, sequencing the genome isn't really understanding it. So for example, if we have three billion letters in a human genome, HCTG, it's hard to know what those mean. The red one here is actually tells us whether or not you can drink milk as an adult. The blue ones are actually chimpanzee locations. So none of us would know that. We don't know why those are in chimpanzee or if they have any meaning, but that's the challenge is to try to read the sequence, which we can come out at an amazing rate. Okay, so what's in the sequence? Well, there's nine billion base pairs, there's 30,000 genes, and genes code for proteins or RNA that do things, or the building blocks of the cells and the tools. In addition, there's about three million or so regulatory elements that control when, where, how much, and under what conditions those genes are expressed. Now the gene products, which we don't fully understand yet, are tools. So some tools we understand, some we know what the tool is, we don't know what its purpose is, sometimes we don't know what the tool is and we know its purpose, and sometimes we have no clue. So this is a real challenge right now in the next couple of years that will allow us to crystallize our knowledge about gene function and then understand the genome. Now of course, we're all familiar with nature and nurture, and a newly minted genetically identical organism is going to have a different experience, different environments, and that's going to change its properties. But we can understand that, as we have for a number of years, because it's really just thinking about the probability or the likelihood that some particular attribute will be manifest. Okay, so we have all this information. A lot of it is actually, it's stuck in scientific papers, and I've been involved in the last few years of figuring out ways to officially get all that information out into a computable form. High throughput experiments with big factories, like the sequencing but other things, generate data that actually go right into the databases, and then we can design experiments, you know, handcrafted experiments to do that. Genes don't act alone, so you can imagine a genetic variance that on their own don't do anything, but put together can confer an amazing talent, like making glass and metal float. We understand those, but there's maybe half a billion potential genetic interactions considered pair-wise. The other thing we can do, though, is we build up our understanding of genetic reactions, is use the fact of evolutionary divergence and conservation, and realize that we can study in one organism that there are connections between two genes in, say, fruit fly and in a roundworm, and then we can predict what those interactions are in the human, and that's very powerful. Okay, so we can read genomes and derive some understanding. In the last year, it's been very inexpensive now to edit the genome of virtually any organism. So that allows us to tweak small aspects of the genetic program and also to test experiments about what those sequences mean. And we also can write genomes, but it's a little more expensive, and the challenge, though, is to think about is what do we want to write? Okay, we know about metabolic engineering, and we can program, and we'll continue to do that, to program bacteria to give them sugar and ammonia, so carbon and nitrogen, and turn out valuable products. You know, that's the basis of biotechnology, and it's been around for 100 years. Okay, we can also get a little more complicated and design biological circuits, where, for example, we can have various implementations of a circuit that would compare two inputs, alpha and beta, and determine whether that cell would generate a heart or a lung. Okay, and those can be implemented, and you sort of have mutual inhibition among the two components. Okay, now if you want to scale that up, you have the problem of composability. In the computer industry, which has really solved this problem with integrated circuits and then VLSI, you can use the same component over and over again, and various people in the world are now figuring out ways in which you can take the same biological circuit and keep it separate and stick it in different places in the cell. Okay, where do we really go with this in the long run? Well, imagine not a magic bean, but you could plant a seed and you could get a chair or a house. We're starting to know enough about plant development and to think about programming those. Okay, so that's one thing I'd like you to imagine. The other thing you can imagine is you can manipulate and engineer consortia of organisms, like bacteria and transparent roundworms are my favorite, and, for example, try to get help the desert bloom or convert a red planet, like Mars, into a green one. Okay, those are going to be really long-term goals, but we can start to think about how to do it. It's not, you know, it doesn't require any new concepts, right? It does require some engineering. Okay, where are we in 2014? We're poids for an explosion. Because the knowledge, even though we're still accumulating it, is crystallized so that we can, it's crystallizing. So I think a little more knowledge, and we're going to be able to read the genomes, so that will enable personalized medicine that we've heard about at this conference, and I'm sure you're all familiar with, and, you know, a vast new playground of biotechnology. Thanks.